Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of V…
Deep Reinforcement LearningEdge-computingManagementAdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising so…
Edge-computingFederated LearningDigital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model
To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challe…
Edge-computingA learning-based solution approach to the application placement problem in mobile edge computing under uncertainty
Placing applications in mobile edge computing servers presents a complex challenge involving many servers, users, and their requests. Existing algorithms take a long time to solve high-dimensional problems with significa…
Edge-computingStochastic OptimizationDistributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC
Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations…
Decision MakingEdge-computingReinforcement Learning (RL)Scheduling+2NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent l…
Activity RecognitionEdge-computingObject TrackingOptical Flow EstimationUncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their comp…
Edge-computingFieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics
Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localisation. We present FieldNet, a novel dee…
Edge-computingobject-detectionObject DetectionShadow Removal+1Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fundamentally important problem, decides w…
Deep Reinforcement LearningEdge-computingReal-Time Multimodal Cognitive Assistant for Emergency Medical Services
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This pap…
Action RecognitionEdge-computingspeech-recognitionSpeech RecognitionEdge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls
An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with no…
Edge-computingOptimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
Deep reinforcement learning(DRL) has shown significant promise in a wide range of applications including computer games and robotics. Yet, training DRL policies consume extraordinary computing resources resulting in dens…
Deep Reinforcement LearningEdge-computingModel CompressionEnhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an…
Automatic Modulation RecognitionEdge-computingEdge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling
Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, a…
Deep LearningEdge-computingTime SeriesArchitectural Blueprint For Heterogeneity-Resilient Federated Learning
This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computa…
Edge-computingFederated LearningPrivacy PreservingMaximizing Energy Charging for UAV-assisted MEC Systems with SWIPT
A Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) scheme with simultaneous wireless information and power transfer (SWIPT) is proposed in this paper. Unlike existing MEC-WPT schemes that disregard the …
Edge-computingSchedulingLeveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks
To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end …
Cloud ComputingDeep Reinforcement LearningEdge-computingFederated Learning+1DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators
Photonic computing has emerged as a promising solution for accelerating computation-intensive artificial intelligence (AI) workloads, offering unparalleled speed and energy efficiency, especially in resource-limited, lat…
Edge-computingTraining Machine Learning models at the Edge: A Survey
Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and infe…
Edge-computingFederated LearningSurveySTAR-RIS Assisted Wireless-Powered and Backscattering Mobile Edge Computing Networks
Wireless powered and backscattering mobile edge computing (WPB-MEC) network is a novel network paradigm to supply energy supplies and computing resource to wireless sensors (WSs). However, its performance is seriously af…
Edge-computing